AI is happening. The future is updating.Ternary-Bonsai-8B-mlx-2bit
> **7.1x** smaller than FP16 | **5.2x** faster on M4 Pro | **27** tok/s on iPhone | runs on Mac, iPhone, iPad The information-theoretic cost is log2(3) ≈ 1.585 bits per weight, plus FP16 group scales (16 bits per 128 weights), for a theoretical minimum of ~1.71 bits/weight. This release uses the MLX 2-bit format, which stores each ternary value in 2 bits plus group scales, for an effective ~2.125 bits/weight. The addition of a zero value compared to binary (1-bit) provides more expressive weight representations, allowing better preservation of model quality under extreme compression.
> **7.1x** smaller than FP16 | **5.2x** faster on M4 Pro | **27** tok/s on iPhone | runs on Mac, iPhone, iPad The information-theoretic cost is log2(3) ≈ 1.585 bits per weight, plus FP16 group scales (16 bits per 128 weights), for a theoretical minimum of ~1.71 bits/weight. This release uses the MLX 2-bit format, which stores each ternary value in 2 bits plus group scales, for an effective ~2.125 bits/weight. The addition of a zero value compared to binary (1-bit) provides more expressive weight representations, allowing better preservation of model quality under extreme compression.